NDLI: Empirical studies on parallel network construction of Bayesian optimization algorithms

Content Provider	IEEE Xplore Digital Library
Author	Masaharu Munetomo Naoya Murao Kiyoshi Akama
Copyright Year	2003
Description	Author affiliation: Inf. Initiative Center, Hokkaido Univ., Sapporo, Japan (Masaharu Munetomo; Naoya Murao; Kiyoshi Akama)
Abstract	This paper discusses a parallel optimization algorithm based on evolutionary algorithms with probabilistic model-building in order to design a robust search algorithm that can be applicable to a wide-spectrum of application problems effectively and reliably. Probabilistic model building genetic algorithm, which is also called estimation of distribution algorithm, is a promising approach in evolutionary computation and its parallelization has been investigated. We propose an improvement of parallel network construction in distributed Bayesian optimization algorithms which estimate distribution of promising solutions as Bayesian networks. Through numerical experiments on an actual parallel architecture, we show the effectiveness of our approach compared to the conventional parallelization. Also we perform experiments on a real-world application problem: protein structure predictions.
Starting Page	1524
Ending Page	1531
File Size	4647547
Page Count	8
File Format	PDF
ISBN	0780393635
DOI	10.1109/CEC.2005.1554870
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-02
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Evolutionary computation Design optimization Robustness Algorithm design and analysis Electronic design automation and methodology Encoding Genetic algorithms Parallel architectures Proteins
Content Type	Text
Resource Type	Article

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Network Optimization based on Genetic Algorithm and Estimation of Distribution Algorithm

Potentialities of Optimal Design Methods for the Development of Mini Parallel Robots Using Genetic Algorithms

Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment

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Differential Evolution Made Faster And More Robust

Estimation of Bayesian network algorithm with GA searching for better network structure

Using fitness distributions to design more efficient evolutionary computations

Exact Bayesian network learning in estimation of distribution algorithms

Modal mutations in evolutionary algorithms

Empirical studies on parallel network construction of Bayesian optimization algorithms

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